Recognition of Typical Highway Driving Scenarios for Intelligent Connected Vehicles Based on Long Short-Term Memory Network

Xinjie Feng, Shichun Yang, Zhaoxia Peng, Yuyi Chen, Bin Sun, Jiayi Lu, Rui Wang, Yaoguang Cao, Yaoguang Cao

2025

Abstract

In the complex traffic environment where intelligent connected vehicles (ICVs) and traditional vehicles coexist, accurately identifying the driving scenarios of a vehicle helps ICVs make safer and more efficient decisions, while also enabling performance evaluation across different scenarios to further optimize system capabilities. This paper presents a typical highway driving scenarios recognition model with extensive scenario coverage and high generalizability. The model first categorizes the constituent elements of driving scenarios and extracts the core elements of typical highway scenarios. Then, based on a long short-term memory (LSTM) network architecture, it extracts features from the ego vehicle and surrounding vehicles to identify the typical driving scenarios in which the ego vehicle is located. The model was tested and validated on the HighD dataset, achieving an overall accuracy of 96.74% for four typical highway scenarios: Lane-change, Car-following, Alongside vehicle cut-in, and Preceding vehicle cut-out. Compared to baseline models, the proposed model demonstrated superior performance.

Paper Citation

in Harvard Style

Feng X., Yang S., Peng Z., Chen Y., Sun B., Lu J., Wang R. and Cao Y. (2025). Recognition of Typical Highway Driving Scenarios for Intelligent Connected Vehicles Based on Long Short-Term Memory Network. In Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-745-0, SciTePress, pages 25-33. DOI: 10.5220/0013201700003941

in Bibtex Style

@conference{vehits25,
author={Xinjie Feng and Shichun Yang and Zhaoxia Peng and Yuyi Chen and Bin Sun and Jiayi Lu and Rui Wang and Yaoguang Cao},
title={Recognition of Typical Highway Driving Scenarios for Intelligent Connected Vehicles Based on Long Short-Term Memory Network},
booktitle={Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2025},
pages={25-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013201700003941},
isbn={978-989-758-745-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - Recognition of Typical Highway Driving Scenarios for Intelligent Connected Vehicles Based on Long Short-Term Memory Network
SN - 978-989-758-745-0
AU - Feng X.
AU - Yang S.
AU - Peng Z.
AU - Chen Y.
AU - Sun B.
AU - Lu J.
AU - Wang R.
AU - Cao Y.
PY - 2025
SP - 25
EP - 33
DO - 10.5220/0013201700003941
PB - SciTePress